{"id":210894,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210894","sets":["6164:6165:6640:10580"]},"path":["10580"],"owner":"44499","recid":"210894","title":["データセット細分化を用いた時系列データ回帰モデル化手法の検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-06-17"},"_buckets":{"deposit":"bd855fcd-3a75-4876-acd9-a408825d22a4"},"_deposit":{"id":"210894","pid":{"type":"depid","value":"210894","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"データセット細分化を用いた時系列データ回帰モデル化手法の検討","author_link":["534800","534799","534801","534802","534798"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"データセット細分化を用いた時系列データ回帰モデル化手法の検討"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"クラウドコンピューティング","subitem_subject_scheme":"Other"}]},"item_type_id":"18","publish_date":"2020-06-17","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_18_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"お茶の水女子大学"},{"subitem_text_value":"株式会社富士通研究所"},{"subitem_text_value":"株式会社富士通研究所"},{"subitem_text_value":"株式会社富士通研究所"},{"subitem_text_value":"お茶の水女子大学"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/210894/files/IPSJ-DICOMO2020181.pdf","label":"IPSJ-DICOMO2020181.pdf"},"date":[{"dateType":"Available","dateValue":"2022-06-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DICOMO2020181.pdf","filesize":[{"value":"1.6 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"950ba2cd-2ace-461e-a174-a5018a055b0e","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2020 by the Information Processing Society of Japan"}]},"item_18_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高橋, 佑里子"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"鈴木, 成人"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"山本, 拓司"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"福田, 裕幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小口, 正人"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_18_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年のクラウドサービスにおいて，サーバを仮想化することで使用率を向上させ，サーバ数を削減する取り組みが行われている．この取り組みでは，サーバが自身の CPU 資源を超えた CPU を割り当てられるオーバーコミット状態に陥ることで，仮想サーバの性能が低下する可能性があるため，制御対象のすべての仮想サーバの CPU 使用率を予測し制御を行う必要がある．本研究では，仮想サーバの CPU 使用率の汎用的な深層学習予測モデルの生成に向けて，時系列データの回帰モデル化手法についての検討を行う．方法を模索した結果，時系列データを学習に必要な長さごとに抽出を行い，細分化した後のデータをランダムに使用することで，再学習時に使用するデータ数の削減が可能であることを確認した．","subitem_description_type":"Other"}]},"item_18_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"1256","bibliographic_titles":[{"bibliographic_title":"マルチメディア，分散協調とモバイルシンポジウム2200論文集"}],"bibliographicPageStart":"1251","bibliographicIssueDates":{"bibliographicIssueDate":"2020-06-17","bibliographicIssueDateType":"Issued"},"bibliographicVolumeNumber":"2020"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T17:59:29.467581+00:00","created":"2025-01-19T01:12:05.621299+00:00","links":{}}